Vice President

Posting Date

01/26/2026

2000 16th Street, Denver, Colorado, 80202, United States of America

DaVita is currently recruiting for a Vice President of Clinical Analytics – a newly created position based in Denver, CO.

General Purpose of the Job

This executive leadership position is responsible for transforming DaVita’s clinical data, reporting and analytics functions.  The Vice President will lead the strategy and execution for clinical data and analytics across our DaVita Kidney Care (“DKC”) and Integrated Kidney Care (“IKC”) teams, providing comprehensive and actionable clinical, operational, and financial information to key stakeholders.

Supervisory Responsibilities

Yes. This position will have full supervisory responsibility for our DKC and IKC Clinical Data and Analytics teams, including recruitment, hiring, performance development, and management of direct reports.

Essential Duties and Responsibilities

1. People & Organizational Leadership

• Talent Strategy & Development: Define and execute a talent strategy to recruit, retain, and develop world-class analytics engineers and business intelligence specialists.

• Organizational Design: Design and evolve a highly effective organizational structure (aligned to the hub-and-spoke model) with clear leveling, career paths, and focused domain/functional teams (e.g., DKC, IKC OR Data Domain).

• Team Efficiency & Velocity: Drive operational efficiencies within the team by streamlining workflows, standardizing best practices (e.g., UAT, reports deployment), and minimizing "data wrangling" time.

• Professional Growth: Champion growth opportunities to elevate the technical of the analytics team.  Outline and support growth of team members for analytics and BI career ladder.

• Culture of Insight: Foster an insights-first culture that promotes intellectual curiosity, challenges assumptions, and embeds data-driven decision-making across the business.

• Executive Translation: Serve as the executive translator between complex analytical outputs and C-suite strategy, ensuring insights are communicated clearly and tied directly to business outcomes.

2. Process & Governance

• Intake & Prioritization: Establish a transparent, centralized intake process and governance body to evaluate, prioritize, and manage the portfolio of analytics projects based on strategic value and return on investment (ROI).

• Roadmap and Portfolio Management: Maintain a high-level, clear quarterly and yearly roadmap view of all analytics initiatives, communicating delivery timelines, dependencies, and business impact to executive stakeholders.

• Product Management & Adoption: Implement strong product management principles for all analytical assets (e.g., dashboards, models) to actively measure user adoption, frequency of usage, and overall satisfaction, driving continuous improvement.

• Service Level Agreements (SLAs): Define and enforce clear Service Level Agreements (SLAs) with business stakeholders for key deliverables, including reporting availability, model refresh rates, and time-to-insight for new requests.

• Capacity Planning & Productivity: Implement rigorous metrics and tools for measuring team capacity, utilization, and productivity, ensuring resources are optimally allocated and mitigating burnout risks.

• Requirement Definition (SMEs): Establish a formal, repeatable process for partnering with key Business Subject Matter Experts (SMEs) to deeply define analytical requirements, validate hypothesis, and co-create impactful business insights.

• Model Governance: Oversee the governance framework for analytical models, ensuring compliance, ethical use, documentation, and a clear path for model monitoring and deprecation.

• Support, Operations, & Value Tracking: Direct the support and operations function to proactively monitor and resolve analytical issues, ensure platform uptime, and systematically track and report on the tangible business value (e.g., revenue increase, cost reduction) generated by insights.

3. Technology & Enablement

• Tool Rationalization & Modernization: Lead the rationalization and modernization of the analytics technology stack (BI, visualization, data science platforms) to reduce redundancy, optimize licensing costs, and improve team efficiency.

• Data Product Consumption: Mandate and ensure the analytics team effectively leverages the "Gold Layer" (curated, governed) assets and data products delivered by the central Data Management